ASIC says superannuation funds are struggling to analyse complaints data
Every so often, regulators take the opportunity to remind us that high-quality data governance in the wider finance sector is a compliance fundamental rather than just “nice to have”.
Last year, it was the Banking Code Compliance Committee publicly sanctioning organisations for their failures to identify and stop charging deceased estates. Recently, ASIC put superannuation funds on notice, saying they analysing complaints data to identify trends, and are instead leaving members to take the blame for systemic issues.
Since 2021, superannuation trustees have been obliged to record data on all complaints, analyse complaints data to identify systemic issues and escalate possible systemic issues so they can be resolved.
ASIC’s own media release quotes ASIC Commissioner Simone Constant:
‘Alarmingly, the Australian Financial Complaints Authority appears better at picking up issues than industry. When comparing the two years of data we collected, AFCA identified systemic issues from superannuation complaints it received at almost twice the rate all trustees identified from the complaints they received.’
Data must be fit for purpose
No business can successfully analyse poorly organised data.
Sometimes common knowledge bears repeating: if garbage data goes into your analyses, garbage insights come out. There is a minimum standard of underlying data sufficiency that’s needed to actually receive business intelligence from a repository of data.
In environments where security is paramount, we often find that stores of data are siloed across departments or units, owing to a need to isolate some data from access. That can result in environments in which the left hand knows not what the right is doing, breeding duplicate records, incomplete customer information and poor overall performance of data. It also creates double- and triple-handling and excessive administration, as staff seek work-arounds for poor integration.
These practices can offer only relatively minor obstructions in the day-to-day activities of data end-users, but when it comes time to analyse broad trends and derive meaningful business intelligence, the outcomes can be very poor indeed.
Data governance ensures performance
Strong data governance is the only solution to poor utilisation of collected data. Without it, it’s almost impossible to collect, maintain, store and use (and delete, at need) the correct data in a state adequate to providing real, trustworthy business intelligence.
In an ideal scenario, organisations treat data governance as a critical requirement. In an organisation with a real data culture, data governance represents a vital store of information, including data strategy and familiar, living documents that provide for every policy, process, escalation procedure, data standard and quality metric that ensures data adequacy.
But data governance is not often a priority for businesses. While everyone “has data governance,” it’s rarely owned by resources for whom it’s the primary concern, and instead of guiding processes, governance becomes a dusty repository for documents nobody recognises, updated with a desultory hand every few years and accessed infrequently.
Your data should serve you
In the above news articles, it’s easy to see the potential fall-out from poor data utilisation: without being able to offer up insight into complaint trends and identify systemic issues, organisations run the risk of being sanctioned for compliance failures.
But many of the outcomes businesses suffer when they don’t use their data well are much less clear-cut. Instead, they look like competitive disadvantages, bad sales and marketing decisions, and confused business intelligence that leads to poor decision-making at a C-suite level.
These can all be vastly improved by making data governance a priority, instead of an afterthought.
